user
Task: How does unsupervised learning work?


assistant
Unsupervised learning is a type of machine learning algorithm that does not require labeled data and uses data exploration techniques in order to learn from unlabeled data. In unsupervised learning, the data is clustered or organized based on similar characteristics. The cluster or group of data is then used to find correlations and patterns between the data, which then become the learned criteria. This process helps the algorithm to learn without any prior training data, which makes it different from supervised learning algorithms. The learned model is then used to make predictions. The predictions made by the unsupervised learning algorithm depend on the data available, and the algorithm's ability to identify correlations and patterns.